Llama 3.3 70B Versatile via Groq
Specifications
Context Window
131,072 tokens
Release Date
2024-12-06
Capabilities
Tool callingTemperature
Availability
Open Weights
Model Overview
Groq provides ultra-fast AI inference powered by their custom LPU (Language Processing Unit) hardware. They host popular open-source models like LLaMA and Mixtral with industry-leading speed and low latency.
Llama 3.3 70B Versatile is a llama-family model by Groq with a 131k token context window and up to 33k output tokens. It is priced at $0.5900/1M input tokens and $0.7900/1M output tokens.
Key capabilities include: tool calling, temperature. It can call external tools and functions for agentic workflows.
Details
ProviderGroq
Model IDllama-3.3-70b-versatile
Familyllama
Release Date2024-12-06
Last Updated2024-12-06
Knowledge Cutoff2023-12
Context Window131,072 tokens
Max Output32,768 tokens
Input Cost / 1M$0.5900
Output Cost / 1M$0.7900
Developer Resources
More models from Groq
Frequently Asked Questions
How much does Llama 3.3 70B Versatile cost to use?
Llama 3.3 70B Versatile is priced at $0.5900/1M input tokens and $0.7900/1M output tokens. Use the cost estimator on this page to calculate your expected spend based on your usage pattern.
What is a token and how does it relate to pricing?
A token is a chunk of text — roughly ¾ of a word in English. For example, "chatbot" is two tokens. LLM API pricing is based on the number of tokens you send (input) and receive (output). Input tokens include your prompts, uploaded documents, and images, while output tokens are the model's generated responses.
Why are input and output tokens priced differently?
LLM providers charge separately for input and output tokens. Output tokens are typically more expensive because generating each token requires more compute — the model must run a full forward pass for every token it produces, while input tokens are processed in parallel.
What is the context window of Llama 3.3 70B Versatile?
Llama 3.3 70B Versatile supports a context window of 131,072 tokens. This is the maximum number of tokens (input + output combined) the model can process in a single request. Larger context windows let you send longer documents or maintain longer conversation histories.
How accurate is this cost estimation?
This tool provides a ballpark estimate based on per-token pricing. Actual costs may differ due to prompt caching, batched API calls, volume discounts, reasoning token overhead, and provider-specific billing rules. Use it for budgeting and comparison, not as an invoice prediction.
How does Llama 3.3 70B Versatile pricing compare to other models?
You can compare Llama 3.3 70B Versatile with other models on our LLM API pricing calculator. Use the cost estimator to see side-by-side cost breakdowns across different providers and models to find the best fit for your budget and requirements.
What factors affect my total API cost?
Your total cost depends on several factors: the number of API calls you make, the length of your prompts (input tokens), the length of generated responses (output tokens), whether you use features like image or document uploads (which add input tokens), and any provider-specific charges for caching or batch processing.
How can my team use Llama 3.3 70B Versatile via API?
You can connect your own Groq API key and give your entire team access to Llama 3.3 70B Versatile through TypingMind Teams. It lets you build a unified AI workspace where team members can use Llama 3.3 70B Versatile and other models — without needing their own API keys. You stay in control of usage limits, costs, and permissions, all from a single dashboard.
